Cybertwin-Driven DRL-Based Adaptive Transmission Scheduling for Software Defined Vehicular Networks

Efficient transmission control is a challenging issue in vehicular networks due to the highly dynamic and unpredictable link status. In this paper, the authors propose a cybertwin-driven learning-based transmission scheduling mechanism for software-defined vehicular networks, which can adaptively select/adjust transmission control methods, i.e., loss-based, delay-based and hybrid ones, to suit to the time-varying network environment. In particular, the authors first analyze the dynamic network characteristics of three realistic vehicular network scenarios in terms of network throughput, round-trip time (RTT) and RTT jitter. Furthermore, the authors propose a novel transmission scheduling model and formulate the SDVN transmission scheduling issue as a linear programming problem. To obtain the optimized scheduling policies and guarantee the effectiveness of transmission control, the authors further propose a Cybertwin-driven and Deep Reinforcement Learning based transmission control solution (TcpCDRL). Specifically, TcpCDRL is featured with: (i) using deep reinforcement learning (DRL) to adaptively adjust transmission control policy, (ii) using cybertwin-driven transmission controlling to improve the policy-making effectiveness and timeliness. Simulation results show that the proposed TcpCDRL approach outperforms the single well-known transmission control approach (i.e., TcpWestwood, TcpBic, TcpVeno and TcpVegas) in terms of network throughput and RTT.

• Record URL:
  http://dx.doi.org/10.1109/TVT.2022.3151750
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00189545
• Supplemental Notes:
  • Copyright © 2022, IEEE.
• Authors:
  • Quan, Wei
  • Liu, Mingyuan
  • Cheng, Nan
  • Zhang, Xue
  • Gao, Deyun
  • Zhang, Hongke
• Publication Date: 2022-5

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 4607-4619
• Serial:
  • IEEE Transactions on Vehicular Technology
  • Volume: 71
  • Issue Number: 5
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 0018-9545
  • Serial URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=25

Subject/Index Terms

• TRT Terms: Data communications; Delays; Machine learning; Motor vehicles; Networks; Software
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01849284
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 23 2022 9:16AM